from demo_DQN import Agent
import gym 
if __name__ == "__main__":
    env = gym.make("Pong-v0")
    env = env.unwrapped
    DQN_net = Agent(load=True,path='model.pth')
    state = env.reset()
    done = False
    while True:
        state = env.reset()
        env.render(mode = "human")
        done = False
        Action_list = []
        while done == False:
            action = DQN_net.make_decision(state,epi=1)
            Action_list.append(action)
            next_state,reward,done,_ = env.step(action)
            state = next_state
            env.render(mode = 'human')
        # print(Action_list)